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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 71-79     DOI: 10.6046/gtzyyg.2020.03.10
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An impervious surface index construction for restraining bare land
CAO Yong1(), TAO Yuxiang1(), DENG Lu1, LUO Xiaobo1,2
1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065,China
2. Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
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Abstract  

At present, the method of extracting the impervious surface area based on the impervious surface area according to the impervious surface spectral index has been widely used because of its concision and speed. However, the method of extracting impervious surface by spectral index has the disadvantage that bare land and impervious surface are easily confused. To tackle this problem, the authors created impervious surface and bareness area index (ISBAI) according to the spectral feature difference of impervious surface, bare land and water body as well as vegetation in the 4, 5 and 6 bands of Landsat8 OLI images. Based on ISBAI and bareness area index (BAI), the authors built a new type of impervious surface index, called the bareness - restrained impervious surface index (BRISI). Improved double-window flexible pace search (IDFPS) method was used to determine the optimal threshold, and impervious surface extraction was performed. Chongqing (a mountain city) and Xi’an (a plain city) were selected as the research area to evaluate the accuracy of BRISI extraction in comparison with other commonly used impervious surface indices. The experimental results show that the extraction accuracy of BRISI in the experimental area of Chongqing and Xi’an experimental area reach 86.8% and 86.8% respectively, in comparison with the accuracy of all other indices that took part in the contrast, BRISI extraction accuracy is the highest. Meanwhile, BRISI also eliminates the influence of bare land in the construction area extraction, and overcomes the problem that it is difficult for other impervious surface indices to distinguish bare land from impervious surface.
Keywords impervious surface      bare land      BRISI(bareness-restrained impervious surface index)      IDFPS(improved double-flexible pace search)     
:  TP79  
Corresponding Authors: TAO Yuxiang     E-mail: 2289909272@qq.com;taoyx@cqupt.edu.cn
Issue Date: 09 October 2020
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Yong CAO
Yuxiang TAO
Lu DENG
Xiaobo LUO
Cite this article:   
Yong CAO,Yuxiang TAO,Lu DENG, et al. An impervious surface index construction for restraining bare land[J]. Remote Sensing for Land & Resources, 2020, 32(3): 71-79.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.10     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/71
Fig.1  Research area image
序号 波段 波长范围/μm 空间分辨率/m
B1 海岸 0.433~0.453 30
B2 蓝光 0.450~0.515 30
B3 绿光 0.525~0.600 30
B4 红光 0.630~0.680 30
B5 近红外 0.845~0.885 30
B6 短波红外1 1.560~1.660 30
B7 短波红外2 2.100~2.300 30
B8 全色 0.500~0.680 15
B9 卷云 1.360~1.390 30
B10 热红外1 10.60~11.19 100
B11 热红外2 11.50~12.51 100
Tab.1  Landsat8 OLI band information
Fig.2  Reflectivity curve of surface object
Fig.3  DN of the ISBAI and BAI indices
研究区 不透水面 裸地 水体 植被
研究区1 1.685 1.288 -0.297 0.511
研究区2 1.861 0.923 -0.192 0.407
Tab.2  Mean values of the BRISI of two research regions
Fig.4  Comparison of thematic information of impervious surface spectral index in research area 1 based on Landsat8 images
Fig.5  Comparison of thematic information of impervious surface spectral index in research area 2 based on Landsat8 images
Fig.6  Thematic information on impervious surfaces based on different spectral indices in research area 1
Fig.7  Thematic information on impervious surfaces based on different spectral indices in research area 2
研究区 指数 阈值 生产者
精度/%		 使用者
精度/%		 总体精
度/%		 Kappa
研究区1 BRISI 0.149 89.7 87.2 86.8 0.724
NDBI -0.069 91.2 62.9 63.6 0.434
IBI 0.152 90.8 76.2 73.1 0.515
CBI 0.254 83.3 80.1 83.5 0.681
EBBI 0.268 83.6 80.8 82.9 0.627
NDISI 0.162 82.3 81.1 82.1 0.693
研究区2 BRISI 0.248 88.3 89.3 88.4 0.729
NDBI 0.026 91.4 59.1 58.2 0.399
IBI 0.469 90.2 63.5 65.1 0.492
CBI 0.245 87.7 83.4 84.7 0.687
EBBI 0.351 82.5 69.3 71.4 0.592
NDISI 0.188 83.1 84.0 82.0 0.655
Tab.3  Accuracy assessment of other indices and BRISI
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